Influenza virus transcription and progeny production are poorly correlated in single cells

Author:

Bacsik David J12ORCID,Dadonaite Bernadeta1ORCID,Butler Andrew1ORCID,Greaney Allison J12,Heaton Nicholas S34,Bloom Jesse D15ORCID

Affiliation:

1. Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center

2. Department of Genome Sciences & Medical Scientist Training Program, University of Washington

3. Department of Molecular Genetics and Microbiology, Duke University School of Medicine

4. Duke Human Vaccine Institute, Duke University School of Medicine

5. Howard Hughes Medical Institute

Abstract

The ultimate success of a viral infection at the cellular level is determined by the number of progeny virions produced. However, most single-cell studies of infection quantify the expression of viral transcripts and proteins, rather than the amount of progeny virions released from infected cells. Here, we overcome this limitation by simultaneously measuring transcription and progeny production from single influenza virus-infected cells by embedding nucleotide barcodes in the viral genome. We find that viral transcription and progeny production are poorly correlated in single cells. The cells that transcribe the most viral mRNA do not produce the most viral progeny and often represent aberrant infections that fail to express the influenza NS gene. However, only some of the discrepancy between transcription and progeny production can be explained by viral gene absence or mutations: there is also a wide range of progeny production among cells infected by complete unmutated virions. Overall, our results show that viral transcription is a relatively poor predictor of an infected cell’s contribution to the progeny population.

Funder

NIAID

Burroughs Wellcome Fund

HHMI

Publisher

eLife Sciences Publications, Ltd

Subject

General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

Reference64 articles.

1. Altair;Altair,2023

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4. Estimating the frequency of multiplets in single-cell RNA sequencing from cell-mixing experiments;Bloom;PeerJ,2018

5. Dms_Variants;Bloom,2021

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